PROJECT SUMMARY/ ABSTRACT The genetic dissection of complex and quantitative traits remains a formidable challenge in basic and biomedical research. Although the yeast Saccharomyces cerevisiae is a potentially powerful model system to address fundamental questions about genetic architecture, its promise has not been fully realized. More specifically, there is a need to develop new tools to reveal insights into the fundamental characteristics of genetic architecture. To this end, in Aim 1, we will develop a powerful mapping population in yeast for the high- resolution genetic dissection of complex and quantitative traits. Specifically, we will create 10,000 progeny from a funnel cross among eight intelligently selected parental strains that captures a substantial proportion of genetic variation segregating in natural isolates of S. cerevisiae. Preliminary analyses demonstrate the power to map variants of weak effect and context dependent effects, such as gene-gene interactions, will be extremely high. Importantly, the large number of meioses will allow extraordinarily high mapping resolution, often at the scale of a single gene or smaller. All 10,000 progeny will be densely genotyped, allowing whole genome sequence data to be accurately imputed. In Aim 2, we will develop new statistical methods for leveraging the inherent power of this experimental cross. More specifically, we will develop new methods for detecting gene-gene interactions and predicting causal variants from heterogeneous sources of data. Finally, in Aim 3 we will use the experimental cross to comprehensively delineate the genetic architecture of a suite of biomedically important phenotypes such as antifungal resistance and biofilm formation. Overall, the mapping population and statistical tools that we develop will enable powerful and comprehensive insights into the genetic architecture of complex and quantitative traits, complement the development of complex crosses in other model organisms, provide new methods for the interpretation of whole-genome sequence data, and yield novel insights into potential therapeutic targets relevant to fungal pathogenesis.